In a computer and network environment, numeric time series data can be collected for a specific target, and text time series data can be acquired for the specific target. In such environment, a method of discovering a pattern which explains a variation of the numeric time series data by the text time series data, and predicting an evaluation target of interest in the next term based on the numeric time series data and text time series data has been studied.
For example, in the stock market, by considering stock prices as numeric time series data, and news articles related to brands as text time series data, a method of discovering a pattern which is extracted from news articles to explain a variation of stock prices, notifying a user of a brand of interest in the next term, and supporting decision-making related to sales by brand of the user has been studied.
As a method of predicting an evaluation target, for example, two methods have been proposed.
The first method characterizes an evaluation target in advance by explicitly given attribute values, generates time series data based on frequencies of events related to occurrence of the evaluation target, and calculates a degree of importance of the evaluation target or those of the attribute values of the evaluation target, so as to extract an important evaluation target in a specific problem domain.
On the other hand, the second method visually recognizably displays the relationship between an evaluation target and specific words to the user by associating the evaluation target and time-dependent changes in frequencies of occurrence of specific words with each other.
However, the aforementioned two methods normally do not pose any problem, but they suffer the following disadvantages according to the examinations of the present inventor.
For example, the first method cannot handle an evaluation target which cannot be characterized in advance since an evaluation target is characterized in advance by explicit attribute values. Also, the first method limits time series data related to an evaluation target to those of events related to occurrence of the evaluation target.
On the other hand, the second method allows the user to visually recognize the relationship between an evaluation target and specific words, but it cannot automatically discover a pattern which can explain the case of occurrence of a specific relationship.
A solution to such problem of the present invention is to provide an evaluation target of interest extraction apparatus and program, which can handle an evaluation target which cannot be characterized in advance, does not limit time series data to those related to occurrence of the evaluation target, and can automatically discover a pattern.